If the idea of The Long Tail has taught us anything, it is that there is a group willing and able to support even the most obscure taste and curiosity. Products and services come in far more flavors than any of us can comprehend as an individual. But as a group, you might say there’s hardly enough to go around.

I’ve seen this best represented in the movie industry. The counts fluctuate but, by one source, it appears that more than 700 movies were delivered to the masses in 2016. This doesn’t count the more amatuer efforts that float along in the lower rungs of the indie scene. Trends suggest over 800 movies in 2018. That’s 15 movies a week.

Last year, Netflix delivered 80 of these movies. That’s more than Disney’s Pixar, Marvel Studios, and Lucasfilm combined. It’s also more than Warner Bros, New Line, Fine Line, Warner Independent, and Picturehouse combined. And it’s twice as many as Sony.

80 movies from Netflix. Something close to 800 total. A lock for every key. A movie for every taste. And then some.

Is there someone out there who will see them all? Maybe. 800 movies equates to 1,600 hours and a full-time movie critic could certainly watch that many movies in a year’s worth of work. No sweat. But that’s their job. For the rest of us, who has the time and desire to watch that many movies? Very few, I imagine.

Taste Groups and Predictable Desires

Netflix, as a marketplace, is a microcosm of the broader film industry. Here we have a portal that offers more than 4,000 movies today. Coincidentally, the total movie count has decreased significantly while the “television” (i.e. serialised) options have increased 3x.

So there are 4,000 movies. How do you choose which one to watch?

I think it’s pretty obvious. You do what every other person does by using this simple five-step process:

Prior to opening the app, you formulate a set of criteria to define your mood and interests at that moment.

You then construct a scoring methodology to consistently weigh each option according to the criteria.

You then peruse the comprehensive alphabetized list of 4,000 titles.

You flag each title that fits the basic description of your criteria.

You then apply the scorecard to all eligible titles and choose, at the end, the one title that garners the highest score.

That’s how you choose what to watch on Netflix, right? That’s what everyone does. It only takes about twelve hours to complete the process. No big deal.

Sarcasm aside, Netflix’s recommendation engine works in this fashion. It’s a lot more dynamic and based on much more than a simple, individualized set of criteria. But it classifies each movie according to a set of descriptions or “tags”, then tracks your selections from each such classification.

From those titles that you choose to watch, the engine records your explicit data (your rating for each title) and implicit data (did you watch the whole thing?) and correlates that with data from other subscribers to develop patterns. Those patterns become taste groups.

The taste group is basically a set of anonymous subscribers who not only share your general interests as a viewer but also your same basic set of criteria for what makes a good movie within those categories of interest. The criteria isn’t something you made, of course. It’s simply evident in your viewing choices over time.

Those taste groups give Netflix a sense of how even the most obscure movie can satisfy the interest of some percentage of subscribers. You, as a subscriber, are associated with many such groups and so, like them, you’ll get recommendations on what to watch next.

So next time you see a category listing of Because You Watched <Title> … just know that the recommended titles under that listing aren’t solely based on your preferences but that of your broader taste group. We are all part of such groups.

The Existential Threat Of Too Many Options

As a side note, I want to highlight the fact that Netflix’s recommendation engine is the most important aspect of its entire business. If there was no quick, easy, reliable way for people to find something enjoyable in that marketplace, Netflix would have the very-real-yet-very-absurd problem that cable faces: there’s nothing on TV.

How is that possible? 57 channels and nothing on? Part of it is about your preference in your small taste group. 57 channels isn’t nearly enough for any entity to offer something that you might want at that given point in time when there are millions of others to also serve. 57 channels is the stuff of the “fat tail” mainstream. A 57 channel live TV scheme means there cannot be more than 57 taste groups at a given moment. No more than 57 broad groups can be served at a time. And there’s usually much less than that. An on-demand portal like Netflix can show something very different. It won’t be 57 channels. More like 30. But it’s your 30. Based on your groupings.

Also, the 57 channel phenomena suffers the paradox of choice. Too many choices creates too much stress. To borrow from the great Schopenhauer,

Limitation always makes for happiness.

So Netflix needs the engine because it helps limit choices while also improving the choices that remain. Based on you and your group’s interests. It reminds me of the Dieter Rams adage: Less but better.

Our Brain As A Recommendation Engine

There is an important line in the early pages of William B. Irvine’s On Desire:

We should learn to sort through our desires, working to fulfill some of them while working to suppress others.

I think we all understand this idea. But Irvine’s book helps because it allows us to understand the nature of these desires. Consider the following:

If we take a census of our desires, we will find that the vast majority of the desires we form are instrumental. Most of the time, we are working toward some goal, the fulfillment of which will enable us to reach that goal.

The content on Netflix is a broad array of instruments. We select any given one of them at any given time based on the terminal desire. We pick the scary movie because we want to feel the thrill. We pick the drama because we want to feel the suspense. We pick the romantic comedy because we want to laugh and cry. And maybe feel some nostalgia, too?

Netflix provides a fascinating, albeit imperfect, system to recommend each of these instruments for your moment’s desires.

Our brain provides a fascinating, albeit imperfect, system to recommend every other instrument for your moment’s desires.

This is about imprinting. And taste groups. And availability.

As a child, I didn’t peruse the entire list of past and present toys to select my favorites based on a predefined set of criteria. No, I desired one set of toys because they were (a) some of the first ones I saw, (b) were enjoyed by my older cousins, who were my role models, and (c) they could be bought at the local store.

It’s so much circumstance. Had I seen other toys first, had a different taste group, and if the store only had other things for sale, I would have entirely different preferences.

Anyway, those initial preferences beget other such preferences. It’s all a generalization, never perfect, but the person who likes Legos as a kid, often by pure circumstance, will probably like puzzles and people who like puzzles will like design and engineering and those people will probably do well in school. All because they got to the Legos first as a kid. Sort of. I bet this is about 70% correct. But don’t take my word for it. Consider this article.

Okay, so early preferences beget later preferences. Sure.

What matters here is the somewhat random and largely circumstantial nature of our desires. The instrumental desires, that is. A lot of people like a lot of different things. For reasons that, in some cases, aren’t really in their control.

I think this means that while we are not defined by our desires, our desires are defined by our circumstances. No one should let their circumstances define them. Therefore, no one should let their desires define them.

Our desires are a product of a vast number of associations that begin with initial exposure to things we didn’t really ask for. And if we can very easily find ourselves desiring something on such an insubstantial basis (e.g., a recommendation engine built on sequence, social proof, and availability), we should be able to stop desiring it just as easily.

One would hope.

Netflix recommends a lot of things to me that I ignore because it’s just a simple machine and very imperfect.